Projections of ICU need by Country

• World map (interactive)
• Tables
  • Projected need for ICU beds
    • Growing countries (transmission rate above 5%)
    • Recovering countries (tranmission rate below 5%)
• Appendix
  • Interactive plot of model predictions and past data
  • Projected Affected Population percentages
  • Methodology

Warning: This dashboard contains the results of a predictive model that was not built by an epidemiologist.

Note: Click a country name to open a search results page for that country’s COVID-19 news.

Based on data up to: 2022-11-10

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World map (interactive)

Hover mouse over map for detailed information.

• Details of estimation and prediction calculations are in Appendix and in Tables, as well as Plots of model predictions.
• Recent cases and Recent deaths refer to cases or deaths in the last 5 days.

Tip: Select columns to show on map to from the dropdown menus. The map is zoomable and draggable.

Tables

Projected need for ICU beds

Countries sorted by current estimated need, split into Growing and Recovering countries by current transmission rate. Only for countries with ICU need higher than 0.1 beds per 100k. More details in Appendix.

Growing countries (transmission rate above 5%)

	Estimated current ICU need per 100k population	Estimated daily transmission rate	Projected ICU need per 100k In 14 days	Projected ICU need per 100k In 30 days	Pre-COVID ICU capacity per 100k
🇬🇷 Greece	6.27	noisy data	5.0 ± 1.9	noisy data	6.0
🇩🇪 Germany	5.26	5.6% ± 1.8%	4.0 ± 0.5	2.8 ± 0.8	29.2
🇦🇹 Austria	5.01	8.9% ± 1.6%	3.7 ± 0.2	2.5 ± 0.3	21.8
🇯🇵 Japan	4.54	7.5% ± 2.2%	5.2 ± 1.3	noisy data	7.3
🇰🇷 South Korea	4.40	12.2% ± 3.9%	4.9 ± 1.2	noisy data	10.6
🇫🇷 France	3.93	13.6% ± 4.5%	3.3 ± 0.5	2.5 ± 0.9	11.6
🇸🇮 Slovenia	3.89	noisy data	2.9 ± 0.6	1.9 ± 0.8	6.4
🇫🇮 Finland	3.29	6.0% ± 0.4%	3.1 ± 0.1	2.8 ± 0.2	6.1
🇨🇭 Switzerland	2.78	noisy data	2.0 ± 0.6	noisy data	11.0
🇱🇻 Latvia	2.25	9.3% ± 2.9%	1.8 ± 0.2	1.3 ± 0.4	9.7
🇧🇳 Brunei	1.75	noisy data	1.6 ± 0.5	noisy data	13.1
🇦🇺 Australia	1.47	noisy data	noisy data	noisy data	9.1
🇱🇹 Lithuania	1.26	noisy data	0.9 ± 0.3	noisy data	15.5
🇨🇱 Chile	1.25	14.5% ± 3.6%	1.3 ± 0.2	1.4 ± 0.6	-
🇨🇦 Canada	1.06	noisy data	noisy data	noisy data	13.5
🇩🇰 Denmark	1.02	8.6% ± 2.1%	0.8 ± 0.1	0.6 ± 0.2	6.7
🇭🇷 Croatia	0.93	noisy data	0.7 ± 0.2	noisy data	-
🇸🇪 Sweden	0.89	7.3% ± 0.5%	0.8 ± 0.0	0.7 ± 0.1	5.8
🇺🇸 US	0.87	noisy data	noisy data	noisy data	34.7
🇧🇪 Belgium	0.83	6.7% ± 1.5%	0.6 ± 0.1	0.4 ± 0.1	15.9
🇬🇧 United Kingdom	0.81	noisy data	noisy data	noisy data	6.6
🇭🇺 Hungary	0.76	noisy data	0.6 ± 0.1	0.4 ± 0.2	13.8
🇵🇹 Portugal	0.73	noisy data	0.6 ± 0.3	noisy data	4.2
🇲🇺 Mauritius	0.67	noisy data	noisy data	noisy data	-
🇨🇿 Czechia	0.60	noisy data	0.4 ± 0.1	0.3 ± 0.1	11.6
🇲🇪 Montenegro	0.55	14.3% ± 3.3%	0.4 ± 0.0	0.3 ± 0.0	-
🇧🇬 Bulgaria	0.51	noisy data	0.4 ± 0.1	noisy data	-
🇮🇸 Iceland	0.48	noisy data	noisy data	noisy data	9.1
🇸🇰 Slovakia	0.46	noisy data	0.3 ± 0.1	0.2 ± 0.1	9.2
🇮🇱 Israel	0.43	13.7% ± 6.1%	0.4 ± 0.1	noisy data	-
🇲🇹 Malta	0.35	5.6% ± 1.5%	0.3 ± 0.0	0.2 ± 0.1	-
🇳🇴 Norway	0.32	noisy data	noisy data	noisy data	8.0
🇮🇪 Ireland	0.31	noisy data	noisy data	noisy data	6.5
🇰🇲 Comoros	0.25	noisy data	noisy data	noisy data	-
🇧🇷 Brazil	0.22	noisy data	0.3 ± 0.1	noisy data	-
🇲🇾 Malaysia	0.22	9.5% ± 1.4%	0.2 ± 0.0	0.2 ± 0.1	3.4
🇧🇭 Bahrain	0.20	16.8% ± 2.9%	0.2 ± 0.0	0.2 ± 0.0	-
🇷🇴 Romania	0.19	8.4% ± 2.1%	0.2 ± 0.0	0.1 ± 0.0	-
🇵🇭 Philippines	0.18	7.3% ± 2.1%	0.2 ± 0.0	0.1 ± 0.0	2.2
🇶🇦 Qatar	0.14	5.1% ± 0.6%	0.1 ± 0.0	0.1 ± 0.0	-
🇹🇹 Trinidad and Tobago	0.13	noisy data	0.1 ± 0.0	noisy data	-

Recovering countries (tranmission rate below 5%)

	Estimated current ICU need per 100k population	Estimated daily transmission rate	Projected ICU need per 100k In 14 days	Projected ICU need per 100k In 30 days	Pre-COVID ICU capacity per 100k
🇹🇼 Taiwan*	7.48	4.8% ± 0.7%	5.9 ± 0.4	4.3 ± 0.6	-
🇸🇬 Singapore	3.25	4.1% ± 1.1%	2.4 ± 0.2	1.6 ± 0.3	11.4
🇮🇹 Italy	3.04	0.0% ± 0.0%	1.6 ± 0.0	0.7 ± 0.0	12.5
🇳🇿 New Zealand	2.67	noisy data	1.6 ± 0.4	noisy data	-
🇱🇺 Luxembourg	1.84	noisy data	1.2 ± 0.3	noisy data	24.8
🇨🇾 Cyprus	1.28	0.0% ± 0.0%	0.7 ± 0.0	0.3 ± 0.0	-
🇷🇸 Serbia	0.73	4.7% ± 0.8%	0.6 ± 0.0	0.4 ± 0.1	-
🇳🇱 Netherlands	0.72	noisy data	0.4 ± 0.1	0.3 ± 0.1	6.4
🇪🇸 Spain	0.65	0.0% ± 0.0%	0.3 ± 0.0	0.1 ± 0.0	9.7
🇪🇪 Estonia	0.61	noisy data	0.4 ± 0.1	0.3 ± 0.1	14.6
🇺🇦 Ukraine	0.40	0.1% ± 0.0%	0.2 ± 0.0	0.1 ± 0.0	-
🇷🇺 Russia	0.38	4.5% ± 0.5%	0.3 ± 0.0	0.2 ± 0.0	8.3
🇫🇲 Micronesia	0.34	0.0% ± 0.0%	0.2 ± 0.0	0.1 ± 0.0	-
🇨🇷 Costa Rica	0.28	0.0% ± 0.0%	0.1 ± 0.0	0.1 ± 0.0	-
🇺🇾 Uruguay	0.18	noisy data	0.1 ± 0.0	noisy data	-
🇵🇱 Poland	0.17	noisy data	0.1 ± 0.0	0.1 ± 0.0	6.9
🇬🇪 Georgia	0.17	0.0% ± 0.0%	0.1 ± 0.0	0.0 ± 0.0	-
🇬🇹 Guatemala	0.16	noisy data	0.1 ± 0.0	0.1 ± 0.0	-

Appendix

Interactive plot of model predictions and past data

Tip: Choose a country from the drop-down menu to see the calculations used in the tables above and the dynamics of the model.

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Projected Affected Population percentages

Top 20 countries with most estimated recent cases. Sorted by number of estimated recent cases during the last 5 days. More details in Appendix.

	Estimated recent cases during last 5 days	Estimated total affected population percentage	Estimated daily transmission rate	Projected total affected percentage In 14 days	Projected total affected percentage In 30 days	Current testing bias
🇺🇸 US	300,422	61.1%	noisy data	61.3% ± 0.3%	61.7% ± 0.8%	1.3
🇯🇵 Japan	279,010	18.6%	7.5% ± 2.2%	19.4% ± 0.3%	20.5% ± 1.1%	1.0
🇰🇷 South Korea	234,629	52.1%	12.2% ± 3.9%	53.5% ± 0.6%	55.2% ± 1.9%	1.0
🇩🇪 Germany	140,545	48.9%	5.6% ± 1.8%	49.4% ± 0.2%	49.7% ± 0.4%	1.0
🇫🇷 France	113,752	73.6%	13.6% ± 4.5%	74.1% ± 0.2%	74.5% ± 0.5%	1.0
🇧🇷 Brazil	99,422	99.1%	noisy data	99.2% ± 0.1%	99.3% ± 0.2%	2.7
🇮🇩 Indonesia	91,483	24.4%	10.5% ± 2.8%	24.5% ± 0.1%	24.8% ± 0.3%	4.0
🇹🇼 Taiwan*	88,667	34.0%	4.8% ± 0.7%	35.1% ± 0.2%	36.1% ± 0.4%	1.0
🇨🇳 China	50,830	0.4%	6.2% ± 2.8%	0.4% ± 0.0%	0.4% ± 0.0%	1.0
🇵🇭 Philippines	39,357	45.3%	7.3% ± 2.1%	45.4% ± 0.0%	45.5% ± 0.1%	8.6
🇵🇪 Peru	38,588	nan%	inf% ± nan%	nan% ± nan%	nan% ± nan%	6.8
🇦🇺 Australia	38,097	41.3%	noisy data	41.8% ± 0.9%	42.3% ± 4.7%	1.0
🇨🇦 Canada	31,437	19.7%	noisy data	19.9% ± 0.3%	20.2% ± 0.8%	2.3
🇬🇧 United Kingdom	28,688	63.6%	noisy data	63.7% ± 0.2%	63.7% ± 0.8%	1.2
🇨🇱 Chile	25,465	62.1%	14.5% ± 3.6%	62.5% ± 0.2%	63.1% ± 0.5%	1.0
🇷🇺 Russia	19,530	53.3%	4.5% ± 0.5%	53.4% ± 0.0%	53.4% ± 0.0%	1.0
🇬🇷 Greece	17,542	61.6%	noisy data	62.2% ± 0.6%	62.7% ± 1.4%	1.0
🇮🇳 India	16,732	19.0%	noisy data	19.0% ± 0.0%	19.0% ± 0.1%	5.1
🇦🇹 Austria	15,024	69.9%	8.9% ± 1.6%	70.4% ± 0.1%	70.8% ± 0.2%	1.0
🇲🇾 Malaysia	13,005	45.4%	9.5% ± 1.4%	45.5% ± 0.0%	45.7% ± 0.1%	1.0

Methodology

• I'm not an epidemiologist. This is an attempt to understand what's happening, and what the future looks like if current trends remain unchanged.
• Everything is approximated and depends heavily on underlying assumptions.
• Projection is done using a simple SIR model (see examples) combined with the approach in Total Outstanding Cases:
  • Growth rate is calculated over the 5 past days by averaging the daily growth rates.
  • Confidence bounds are calculated by from the weighted standard deviation of the growth rate over the last 5 days. Model predictions are calculated for growth rates within 1 STD of the weighted mean. The maximum and minimum values for each day are used as confidence bands.
  • Transmission rate, and its STD are calculated from growth rate and its STD using active cases estimation mentioned above.
  • For projections (into future) very noisy projections (with broad confidence bounds) are not shown in the tables.
  • Where the rate estimated from Total Outstanding Cases is too high (on down-slopes) recovery probability if 1/20 is used (equivalent 20 days to recover).
• Total cases are estimated from the reported deaths for each country:
  • Each country has a different testing policy and capacity and cases are under-reported in some countries. Using an estimated IFR (fatality rate) we can estimate the number of cases some time ago by using the total deaths until today.
  • IFRs for each country is estimated using the age adjusted IFRs from International IFRS study and UN demographic data for 2020. These IFRs can be found in df['age_adjusted_ifr'] column.
  • The average fatality lag is assumed to be 8 days on average for a case to go from being confirmed positive (after incubation + testing lag) to death. This is the same figure used by "Estimating The Infected Population From Deaths".
  • Testing bias adjustment: the actual lagged case fatality rate is then divided by the age adjusted IFR to estimate the testing bias in a country. To account for testing bias changes (e.g. increased testing capacity) this is done on a rolling window basis of two months (with at least 300 deaths). The estimated testing bias then multiplies the reported case numbers for each date to estimate the true case numbers (=case numbers that would be consistent with the deaths and the age adjusted IFR).
• ICU need is calculated and age-adjusted as follows:
  • UK ICU ratio was reported as 4.4% of active reported cases.
  • Using UKs ICU ratio, UK's testing bias, and IFRs corrected for age demographics we can estimate each country's ICU ratio (the number of cases requiring ICU hospitalisation).
  • Active cases for ICU estimation are taken from the SIR model.
  • Pre COVID-19 ICU capacities are from Wikipedia (OECD countries mostly) and CCB capacities in Asia. The current capacities are likely much higher as some countries already doubled or even quadrupled their ICU capacities.